Developing translational medicine professionals: the Marie Skłodowska-Curie action model
Bibliographic record
Abstract
End goal of translational medicine is to combine disciplines and expertise to eventually promote improvement of the global healthcare system by delivering effective therapies to individuals and society. Well-trained experts of the translational medicine process endowed with profound knowledge of biomedical technology, ethical and clinical issues, as well as leadership and teamwork abilities are essential for the effective development of tangible therapeutic products for patients. In this article we focus on education and, in particular, we discuss how programs providing training on the broad spectrum of the translational medicine continuum have still a limited degree of diffusion and do not provide professional support and mentorship in the long-term, resulting in the lack of well established professionals of translational medicine (TMPs) in the scientific community. Here, we describe the Marie Skłodowska-Curie Actions program ITN-EUtrain (EUropean Translational tRaining for Autoimmunity & Immune manipulation Network) where training on the Translational Medicine machinery was integrated with education on professional and personal skills, mentoring, and a long-lasting network of TMPs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.051 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.013 | 0.053 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".